Generally, a pipeline system provides a continuous pipe conduit, complete with equipment such as valves, compressor stations, communications systems, and meters, for transporting liquid or gaseous materials from one point to another, usually from one point (or points) of production or processing to another, or to points of use. For example, FIG. 1 illustrates a dual-pipeline system that includes both a gaseous nitrogen pipeline running in parallel to a gaseous oxygen pipeline. A number of plants are shown at various locations along the pipeline route, at which air separation units, compressors, boosters, regulators, and other equipment elements operate to introduce material into the pipeline.
Optimizing the operations of a pipeline system, such as the one shown in FIG. 1, is a complex task. In particular, optimizing a pipeline system to account for power costs of various nodes, for reconfigurable elements (e.g., a pipeline element that can act to increase pressure or flow, or decrease pressures or flows at a particular node of the pipeline), and for contractually obligated output and pressure requirements and at various nodes, has proven to be a difficult task. Commercially-available pipeline optimization systems have been generally unable to provide satisfactory solutions for configuring complex pipeline systems. In particular, commercially-available pipeline optimization systems have been unable to account for the variety of factors that may impact on the operational cost of operating the pipeline. For example, currently available optimization systems have not accounted for a pipeline system configured to transport more than one material (e.g., dual-pipeline system shown in FIG. 1); they have not accounted for all the features of client contracts (e.g., both “take or pay contracts” from the production side of pipeline operations and minimum pressure/flow rates from the delivery side of pipeline operations); and they did not optimize the configuration of reconfigurable network elements. Instead, such systems often constrain a variety of these variables and seek to optimize an isolated aspect of pipeline operation, assuming that the others may be optimized independently. Often, this has lead to sub-optimal solutions. Moreover, even when current systems have proven capable of identifying a highly-quality solution, they often fail to do so in a reasonable amount of time.
Accordingly, there remains a need for techniques for optimizing the operations of a pipeline system. Typically, the optimization process should be used to identify an allowable state of pipeline operations that satisfies any operational requirements, physical abilities, and that minimizes operational costs, most notably power consumption.